Green human resource management and green supply Chain Management on Sustainable performance of nickel mining companies in Indonesia
Bibliographic record
Abstract
The green economy with Islamic perspective is the primary discussion point in this study since it is seen as a potential solution to the current economic and environmental concerns. This study aims to analyze the Sustainability Performance of a nickel mine in North Maluku using Islamic economic perspective. A total of 80 mining workers in North Maluku were respondents in the study who were selected using the convenience sampling method. The results of data processing using structural equation modeling show that five of the six proposed hypotheses are empirically proven, the findings in this study indicate that the Sustainability Performance of nickel mining companies in North Maluku can be achieved through the practice of Green Human Resource Management which forms Health, Safety, Environmental Culture, and Green Supply Chain Management practices. The managerial implication of the findings in this study is that mining companies in North Maluku pay attention to recruitment supervision, training, and impose a system of rewards and punishments for workers related to safe work behavior and environmental preservation. In addition, the company also reports holding regular meetings with workers to socialize company policies related to work safety and environmental preservation. Finally, a license to carry out ISO certification to support the implementation of a supply chain that is environmentally sound and to have and carry out environmental program audits and support environmental-related regulations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".